| Field | Before | After |
|---|---|---|
| Field Trial Status | Before on_going | After completed |
| Field Abstract | Before This project evaluates a randomized controlled trial of an education support program for girls on rationality and preferences. Between October of 2011 and May of 2012 a baseline survey of 7,971 secondary students in school cohorts 9-11 was implemented across 124 classrooms in 33 public secondary schools in Malawi. One intervention arm of the study was targeted towards the 3,997 female students in the sample and randomly provided to a subgroup one-year tuition support and monthly cash stipends. For this study we have selected the 2811 students who were in grade 9 and 10 in 2012 (since those in grade 11 have graduated and are harder to track). This study aims to understand the impact of this education intervention on rationality and preferences in decision making under risk and over time. These outcomes will be measured using experimental methods based on previous work by one of the authors of the study (Choi, Fisman, Gale, and Kariv, AER, 2007; Choi, Kariv, Müller, and Silverman, AER, 2014). | After This project evaluates a randomized controlled trial of an education support program for girls on rationality and preferences. Between October of 2011 and May of 2012 a baseline survey of 7,971 secondary students in school cohorts 9-11 was implemented across 124 classrooms in 33 public secondary schools in Malawi. One intervention arm of the study was targeted towards the 3,997 female students in the sample and randomly provided to a subgroup one-year tuition support and monthly cash stipends. For this study we have selected the 2811 students who were in grade 9 and 10 in 2012 (since those in grade 11 have graduated and are harder to track). This study aims to understand the impact of this education intervention on rationality and preferences in decision making under risk and over time. These outcomes will be measured using experimental methods based on previous work by one of the authors of the study (Choi, Fisman, Gale, and Kariv, AER, 2007; Choi, Kariv, Müller, and Silverman, AER, 2014). [Added in 2019 Dec] Further, we implemented phone surveys in 2017 (3rd follow-up) and 2019 (4th follow-up). In these surveys, we measure labor market outcomes and post-schooling training, marital status and partner information, sexual relationships, and attitudes toward male circumcision, pregnancy and contraceptive usage (females only). In the 2019 survey, we additionally introduced two types of outcome variables to understand the impacts of secondary school education on 1) political preference and participation, and 2) health knowledge and health investment behaviors. The first set of outcome variables capture political preferences and participation. We measured preferences at the second follow-up survey including 1) interest in politics, 2) views on various political issues, and 3) views on political systems, democracy, and elections. We also ask questions on participation in politics in the fourth follow-up survey, which was implemented right after the nation-wide protest related to election fraud in mid 2019. The second set of outcome variables are health knowledge and investment behaviors. We introduce a simple test to measure level of knowledge about malaria. We also provide an opportunity for study participants to pick up a free multi vitamin (which is worth about $10) in designated shops in the city center to measure health investment behavior. |
| Field Last Published | Before May 16, 2016 02:34 PM | After December 12, 2019 12:03 PM |
| Field Primary Outcomes (End Points) | Before Main outcome variables include 1) measures of rationality and preferences, and 2) HIV and HSV2 infection. 1. Rationality: We use revealed preferences analysis to check whether subjects’ behavior comply with utility maximization hypothesis. It will be done by checking whether individual behavior is consistent with Generalized Axiom of Revealed Preferences (GARP) and measuring the extent to which it violates GARP. We use various indices of measuring GARP violations, including Afriat’s Critical Cost Efficiency Index (CCEI). This will be separately done in domains of decision making under risk as well as of intertemporal choices. 2. Risk preferences: Experimental data of choices under risk will allow us to measure two distinct parameters of risk attitudes-utility curvature and probability weighting. This will be based on parametric estimation of rank-dependent utility model (Quiggin, 1981; Schmeidler, 1989). We will also use a single, nonparametric measure of risk attitudes. 3. Time preferences: Using experimental data of intertemporal choices, we will measure two parameters of time impatience-present bias and standard discount rate. This will be based on parametric estimation of quasi-hyperbolic discounting utility model (Laibson, 1997). Also, a single, nonparametric measure of time impatience will be used. 4. HIV and HSV2 infection: We will measure infection of HIV and HSV2 by the rapid test kits. | After Main outcome variables include 1) measures of rationality and preferences, and 2) HIV and HSV2 infection. 1. Rationality: We use revealed preferences analysis to check whether subjects’ behavior comply with utility maximization hypothesis. It will be done by checking whether individual behavior is consistent with Generalized Axiom of Revealed Preferences (GARP) and measuring the extent to which it violates GARP. We use various indices of measuring GARP violations, including Afriat’s Critical Cost Efficiency Index (CCEI). This will be separately done in domains of decision making under risk as well as of intertemporal choices. 2. Risk preferences: Experimental data of choices under risk will allow us to measure two distinct parameters of risk attitudes-utility curvature and probability weighting. This will be based on parametric estimation of rank-dependent utility model (Quiggin, 1981; Schmeidler, 1989). We will also use a single, nonparametric measure of risk attitudes. 3. Time preferences: Using experimental data of intertemporal choices, we will measure two parameters of time impatience-present bias and standard discount rate. This will be based on parametric estimation of quasi-hyperbolic discounting utility model (Laibson, 1997). Also, a single, nonparametric measure of time impatience will be used. 4. HIV and HSV2 infection: We will measure infection of HIV and HSV2 by the rapid test kits. 5. Political preference and participation: We measure 1) interest and participation in politics, 2) views on various political issues, and 3) views on political systems, democracy, and elections in the second follow-up survey. In the fourth follow-up survey, we additionally measure participation in recent nation-wide protests related to election fraud. 6. Health knowledge and investment behavior: We measure respondents’ level of knowledge about malaria. We also provide an opportunity for study participants to pick up a free multi vitamin (which value is about $10) in a designated shop to measure health investment behavior. |
| Field | Before | After |
|---|---|---|
| Field Document | Before |
After
20191201+AEA+RCT+Registry+Preanalysis+Plan+II+-+FINAL.docx
MD5:
128f65e52b1d28ff41feafb02236d288
SHA1:
a71094cf3d9b354410874f13f15457933876acac
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| Field Title | Before | After Pre-analysis plan (Second) |
| Field | Before | After |
|---|---|---|
| Field IRB Name | Before | After Malawi National Health Science Research Committee (NHSRC) |